return inputs
-training_set_size = 100
-training_set = generateData(training_set_size)
+training_set_size = 150
+training_set = generateData2(training_set_size)
data = np.array(training_set)
X = data[:, 0:2]
Y = data[:, -1]
classification_error = 0
for i in range(X.shape[0]):
if Y[i] * np.dot(w, X[i]) <= 0:
- classification_error = classification_error + 1
+ classification_error += 1
w = w + Y[i] * X[i]
return w
def complete(sample):
- sample = np.expand_dims(sample, axis=0)
- return sample
+ new_sample = np.insert(sample, len(sample[0]), [1], axis=1)
+ return np.array(new_sample)
+X = complete(X)
w = perceptron_nobias(X, Y)
-pl.plot([-1, 1], [w[0] / w[1], -w[0] / w[1]])
+# w is orthogonal to the hyperplan
+# with generateData
+# pl.plot([-1, 1], [w[0] / w[1], -w[0] / w[1]])
+# with generateData2 and complete
+# FIXME: the hyperplan equation is not correct
+pl.plot([0, -1 / w[1]], [w[0] / w[1] - 1 / w[1], -w[0] / w[1] - 1 / w[1]])
pl.scatter(X[:, 0], X[:, 1], c=Y, s=training_set_size)
+pl.title(u"Perceptron - hyperplan")
pl.show()